Date: 4 May 2026
Time: 9.30am - 4.30 pm
Prerequisites: None

Overview: R is widely used and extremely powerful statistical software. This course assumes that you have never used R before. You will learn how to obtain and install R, which is open-source software, and RStudio, which is a versatile, user-friendly interface for using R.  The course is self-paced and focused on developing practical skills.

Date: 6 - 8 May 2026
Time: 9.30am - 4:00pm
Prerequisites: Participants must have basic R skills prior to workshop

Overview: This workshop is designed as an introduction to statistical analysis for researchers. There is emphasis on understanding the concepts of statistical procedures (with a minimum of mathematics, although some will be discussed) and on interpreting computer output. It is designed to help you, the researcher. It is helpful if you have done an undergraduate statistics subject, although this workshop can serve as a first introduction or a refresher. The theory behind the statistical procedures will, in general, not be discussed.

Date: 10 - 12 February 2026
Time: 9:30am - 4:00pm
Prerequisites: We assume knowledge of introductory statistics (see description below for more details).

Overview: This course provides a comprehensive hands-on introduction to regression analysis techniques The course content is designed for researchers with some prior knowledge of basic statistical testing, such as t-tests, p-values, confidence intervals and simple linear regression. The primary focus is on developing a conceptual understanding of regression models through numerous examples. There will be a strong emphasis on practical implementation in R, and interpretation of output. Approximately half the time will be dedicated to practical hands-on sessions.

Date: 24 - 25 March 2026
Time: 9:30am - 4:00pm
Prerequisites: Basic knowledge of R and RStudio (completion of our Introduction to R or quick Task)

Overview: This course offers a practical introduction to meta-analysis: statistical methods used in order to pool and synthesize results across multiple studies. It is designed for researchers and students who want to learn how to fit, check, interpret, and communicate results from meta-analyses. Through lectures and exercises, participants will gain hands-on experience with common meta-analytic models and tools, equipping them to apply these methods in their own research.

Next date: 12 - 13 May 2026
Time: 9:30am - 4:00pm
Prerequisites: None

Overview: Simple statistical methods (t-tests, chi-square tests, linear models/regression) assume independence of observations and cannot be used when dependence is present in the sample.

Common reasons for dependencies include:

• Clustering e.g. multiple patients per hospital, multiple plants per site, multiple measurements on the same individual.
• Time and space - measurements taken close in time and space are more similar.

Mixed models are extensions of linear models to dependent data. The course will explain how to fit, check, interpret and communicate results from mixed models.

Date: 27 May 2026
Time: 9:30am - 4:00pm
Prerequisites: No knowledge of statistical software is required.

Overview: Sample size determination is a central component of planning a scientific study. A well-justified sample size ensures that a study is scientifically credible, ethically defensible, and feasible to conduct.

This one-day course introduces the principles of precision-based and power-based sample size calculations. Using practical examples and simulation-based demonstrations, we will outline the general process underlying sample size determination and show how it can be applied across common study designs. We will also discuss practical challenges that arise in real studies, common sources of uncertainty and complexity, and how to report a sample size calculation in a grant application or study protocol.

Date: July 2026 (TBA)
Time: 
Prerequisites: Intermediate R users (have been using R for at least 6 months, comfortable loading data, using basic ggplot and dplyr)

Overview: In this course, we teach students techniques for scaling up their analyses. How do you get R to repeat tasks in an efficient way? This general skill is needed in diverse contexts, such as processing lots of files (e.g. from imaging or surveys), working with large datasets, or simulations (statistical or individual-based).

In these circumstances, you'll often find yourself having to repeat the same thing over and over and it's good to learn how to get R to do this in an organised and efficient manner.